A Novel Transductive SVM for Semisupervised Classification of Remote Sensing Images

摘要

This paper introduces a semisupervised classification method, which exploits both labeled and unlabeled samples, for addressing "ill-posed" problems with support vector machines (SVMs). The method is based on recent developments in statistical learning theory concerning transductive inference and in particular Transductive SVMs (TSVMs). We propose a novel modified TSVM classifier designed for the analysis of "ill-posed" remote-sensing problems. In particular, the proposed technique: ⅰ) is based on a novel transductive procedure that exploits a weighting strategy for the unlabeled patterns based on a time-dependent criterion; ⅱ) is developed also for multiclass cases; and ⅲ) addresses the model-selection problem with lack of test/validation sets. Experimental results confirm the effectiveness of the proposed method on a set of "ill-posed" remote-sensing classification problems representing different operative conditions.